AI-Agent-for-Personalized-Learning: A Personalized AI Learning Assistant Based on Large Language Models

Introducing AI-Agent-for-Personalized-Learning, an entry for the 15th China Software Cup developed by the DisasterGd team. This project builds a personalized learning assistant based on existing large language models, aiming to solve the "one-size-fits-all" problem in traditional education. It provides customized learning support by combining LLM capabilities with technologies like RAG. This article will discuss aspects including background, project overview, technical solutions, and social value.

Traditional education faces the conflict between unified progress and individual differences, leading to low efficiency. The concept of personalized learning needs to address issues such as learner state assessment, knowledge blind spot prediction, and dynamic content adjustment. The natural language understanding/generation capabilities, multi-turn dialogue abilities of large language models (LLMs), along with technologies like prompt engineering and fine-tuning, provide new ideas for personalized learning.

AI-Agent-for-Personalized-Learning is an entry submitted by the DisasterGd team to the 15th China Software Cup. It is a practical learning assistance tool (not a simple chatbot) built based on LLMs. The China Software Cup is hosted by the Ministry of Industry and Information Technology, the Ministry of Education, etc. Entries need to have high technical standards and innovative value, reflecting the team's comprehensive capabilities.

A personalized learning system needs to overcome four major challenges: learner modeling (user profile construction), content adaptation (dynamic content adjustment), interaction design (balancing professionalism and approachability), and learning path planning (progressive knowledge construction). LLMs can address these challenges through dialogue generation, reasoning analysis, content customization, and path recommendation.

Speculated technical solutions the project may adopt: 1. Basic model selection: Open-source LLMs (such as Chinese-friendly models like Qwen, ChatGLM) to facilitate cost control and domain fine-tuning; 2. RAG architecture: Combine external knowledge bases (textbooks, question banks) to solve knowledge cutoff and hallucination issues; 3. Dialogue management module: Maintain coherence of multi-turn contexts; 4. User profile storage: Record learning trajectories to support personalized recommendations; 5. Evaluation and feedback system: Provide error explanations and improvement suggestions.

Social value: Supplement high-quality educational resources, support lifelong learning, and assist special education. Ethical considerations: 1. Data privacy: Strictly protect sensitive learning data; 2. Algorithm fairness: Avoid biases in training data; 3. Over-reliance: Design scaffolding mechanisms to encourage independent thinking; 4. Lack of interpersonal interaction: Position as a supplement to human teachers, retaining scenarios for emotional and value guidance.

This project reflects college students' innovative spirit in AI applications and the concept of technology for good. Insights: 1. Practitioners: Open-source LLMs lower the threshold for educational AI, and RAG is suitable for knowledge-intensive applications; 2. Learners: Future learning will be more autonomous and personalized; 3. Open-source community: Accumulate experience in LLM domain implementation.